Τhis paper focuses on the performance of three alternative Value-at-Risk (VaR) models to provide suitable estimates for measuring and forecasting market risk. The data sample consists of five international developed and emerging stock market indices over the time period from 2004 to 2008. The main research question is related to the performance of widely-accepted and simplified approaches to estimate VaR before and after the financial crisis. VaR is estimated using daily data from UK (FTSE 100), Germany (DAX30), USA (S&P500), Turkey (ISE National 100) and Greece (GRAGENL). Methods adopted to calculate VaR are: 1) EWMA of Riskmetrics, 2) classic GARCH(1,1) model of conditional variance assuming a conditional normally distributed returns and 3) asymmetric GARCH with skewed Student-t distributed standardized innovations. The results indicate that the widely accepted and simplified ARCH framework seems to provide satisfactory forecasts of VaR not only for the pre-2008 period of the financial crisis but also for the period of high volatility of stock market returns. Thus, the blame for financial crisis should not be cast upon quantitative techniques, used to measure and forecast market risk, alone.

The present study adds to the literature on the impact of fiscal policy on business cycle synchronisation. Specifically, it investigates the effects of fiscal policy on business cycle synchronisation between the 10 EMU member-countries and the aggregate EMU12-wide business cycle, using a time-varying framework. The findings suggest that fiscal policy has important effects on business cycle synchronisation for all 10 EMU countries. Hence, fiscal policy is shown to have the potential to be supportive of macroeconomic stabilisation in the Eurozone. However the evidence reveals that none of the countries under examination consistently use fiscal policy to promote business cycle synchronisation.

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Τhis paper focuses on the performance of three alternative Value-at-Risk (VaR) models to provide suitable estimates for measuring and forecasting market risk. The data sample consists of five international developed and emerging stock market indices over the time period from 2004 to 2008. The main research question is related to the performance of widely-accepted and simplified approaches to estimate VaR before and after the financial crisis. VaR is estimated using daily data from UK (FTSE 100), Germany (DAX30), USA (S&P500), Turkey (ISE National 100) and Greece (GRAGENL). Methods adopted to calculate VaR are: 1) EWMA of Riskmetrics, 2) classic GARCH(1,1) model of conditional variance assuming a conditional normally distributed returns and 3) asymmetric GARCH with skewed Student-t distributed standardized innovations. The results indicate that the widely accepted and simplified ARCH framework seems to provide satisfactory forecasts of VaR not only for the pre-2008 period of the financial crisis but also for the period of high volatility of stock market returns. Thus, the blame for financial crisis should not be cast upon quantitative techniques, used to measure and forecast market risk, alone.

The study provides evidence in favour of the price range as a proxy estimator of volatility in financial time series, in the cases that either intra-day datasets are unavailable or they are available at a low sampling frequency.A stochastic differential equation with time varying volatility of the instantaneous logreturns process is simulated, in order to mimic the continuous time diffusion analogue of the discrete time volatility process. The simulations provide evidence that the price range measures are superior to the realized volatility constructed at low sampling frequency. The high-low price range volatility estimator is more accurate than the realized volatility estimator based on five, or less, equidistance points in time. The open-high-low-close price range is more accurate than the realized volatility estimator based on eight, or less, intraperiod log-returns.JEL classification: C15, C53, G17.